Top 5 Python NLP Libraries Every Budding Researcher Should Know

Do you want to find out which are the best frameworks or libraries for natural language processing (NLP) in Python? Do you want to mine the social web and summarise blog posts? There are a lot of NLP libraries on the internet, but finding the right fit for your project is difficult.

In this article, we list down some of the most popular NLP libraries that every budding researcher should know and work with:

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Natural Language Toolkit is one of the most popular platforms for building Python programs. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenisation, stemming, tagging, parsing, and semantic reasoning. It also has wrappers for industrial-strength NLP libraries, and an active discussion forum. If you are a beginner, this is the best library to start with.

Here are some of the tasks you can do with NLTK:

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  • Tokenise and tag text
  • Identify named entities
  • Display a parse tree

Advantage: This is by far one of the most mature platform and a great educational resource and a defacto library for NLP engineers. Natural Language Toolkit comes with a free book which includes extensive data and documentation on how to work with NLTK. It is a must-have for beginners who want to take a deep dive into computational linguistics. It is also good for those who have no prior programming experience in Python.

Here’s how one can install NLTK


This library is quickly gaining ground and is said to overtake NLTK in popularity. It’s fast, accurate, easy to implement and also works well with other tools like TensorFlow, Sickit-Learn, PyTorch and Gensim. This library also provides models for Named Entity Recognition, Dependency Parsing and Part of Speech tagging. This open-source library is also the best way to prepare text for deep learning. Some of its other features include pre-trained word vectors, support for 31+ languages and easy model packaging and deployment.

Advantage: State-of-the-art speed is the best unique feature and spaCy v2.0 features neural models for tasks such as tagging, parsing and entity recognition. Besides being lightning fast, it is highly accurate and easy to run.

Here’s how one can install spaCy


This library was developed and maintained by Czech researcher Radim Řehůřek. Being on a more specialised side, Gensim is primarily used for semantic analysis, document indexing and topic modelling. While it is fast and scalable, it is not for all-purpose tasks like NLTK. Some of its key features are an intuitive interface — for example, it is easy to extend with Vector Space algorithms. It also features Jupyter Notebook tutorials and extensive documentation. Before installing Gensim, you need to have two Python packages in place — Scipy and NumPy.

Advantage: While it is not an all-purpose library like NLTK, it is quite fast and memory efficient. In fact, memory efficiency is pegged to be its key feature and the open source software makes use of Python’s built-in generators and iterators for streamed data processing.

Here’s how you can install Gensim


Beginner-friendly with an easy to use interface, TextBlob is a mining tool very popular among developers for sentiment analysis and a host of NLP-related tasks. In fact, TextBlob is often compared to NLTK. One of the key features of TextBlob is that it has a fairly simple learning curve, as opposed to other open source libraries. The open source software also provides simple APIs for a host of NLP tasks such as classification, translation, part-of-speech tagging, sentiment analysis, phrase extraction, textual analysis and more. If you want to tackle basic NLP tasks, go for TextBlob.

Advantage: Since TextBlob builds on NLTK, it is an easy to use interface and is quite easy for a beginner to understand. If you want to work on basic NLP tasks, TextBlob is the best open source software. In fact, TextBlob performs better than NLTK for textual analysis.

Here’s how you can install it  


Now, Pattern is a web mining module which offers a set of tools for mining the web. It tackles a host of NLP tasks such as tagger/chunker, n-gram search, sentiment analysis, WordNet. It can also deal with machine learning tasks like vector space model, k-means clustering, Naive Bayes + k-NN + SVM classifiers) and network analysis (graph centrality and visualisation). It is maintained by CLiPS Computational Linguistics Group, the University of Antwerp and the library is packed with 30+ examples and 350+ unit tests. While it is more related to NLP toolkits like NLTK or even PYBrain, this library provides cross-domain functionality.

Advantage: It is primarily a web mining library (module) for Python that can be used to crawl and parse Google, Twitter, and Wikipedia. It is useful for both scientific and non-scientific users and has a short development cycle. Currently, Pattern supports Python 2.7 and Python 3.6+.

For installation, click here

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